OpenRaiser/Intern-Atlas huggingface dataset:
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning
Venue: arXiv cs.LG 2009
Year: 2009

Several recent studies in privacy-preserving learning have considered the trade-off between utility or risk and the level of differential privacy guaranteed by mechanisms for statistical query processing. In this paper we study this trade-off in private Support Vector Machine (SVM) learning. We present two efficient mechanisms, one for the case of finite-dimensional feature mappings and one for potentially infinite-dimensional feature mappings with translation-invariant kernels. For the case of translation-invariant kernels, the proposed mechanism minimizes regularized empirical risk in a random Reproducing Kernel Hilbert Space whose kernel uniformly approximates the desired kernel with high probability. This technique, borrowed from large-scale learning, allows the mechanism to respond with a finite encoding of the classifier, even when the function class is of infinite VC dimension. Differential privacy is established using a proof technique from algorithmic stability. Utility--the mechanism's response function is pointwise epsilon-close to non-private SVM with probability 1-delta--is proven by appealing to the smoothness of regularized empirical risk minimization with respect to small perturbations to the feature mapping. We conclude with a lower bound on the optimal differential privacy of the SVM. This negative result states that for any delta, no mechanism can be simultaneously (epsilon,delta)-useful and beta-differentially private for small epsilon and small beta.
Learning Mixtures of Gaussians using the k-means Algorithm
Venue: arXiv cs.LG 2009
Year: 2009

One of the most popular algorithms for clustering in Euclidean space is the $k$-means algorithm; $k$-means is difficult to analyze mathematically, and few theoretical guarantees are known about it, particularly when the data is {\em well-clustered}. In this paper, we attempt to fill this gap in the literature by analyzing the behavior of $k$-means on well-clustered data. In particular, we study the case when each cluster is distributed as a different Gaussian -- or, in other words, when the input comes from a mixture of Gaussians. We analyze three aspects of the $k$-means algorithm under this assumption. First, we show that when the input comes from a mixture of two spherical Gaussians, a variant of the 2-means algorithm successfully isolates the subspace containing the means of the mixture components. Second, we show an exact expression for the convergence of our variant of the 2-means algorithm, when the input is a very large number of samples from a mixture of spherical Gaussians. Our analysis does not require any lower bound on the separation between the mixture components. Finally, we study the sample requirement of $k$-means; for a mixture of 2 spherical Gaussians, we show an upper bound on the number of samples required by a variant of 2-means to get close to the true solution. The sample requirement grows with increasing dimensionality of the data, and decreasing separation between the means of the Gaussians. To match our upper bound, we show an information-theoretic lower bound on any algorithm that learns mixtures of two spherical Gaussians; our lower bound indicates that in the case when the overlap between the probability masses of the two distributions is small, the sample requirement of $k$-means is {\em near-optimal}.
Robust Cooperative Spectrum Sensing for Disaster Relief Networks in Correlated Environments
Venue: arXiv cs.NI 2009
Year: 2009

Disaster relief networks are designed to be adaptable and resilient so to encompass the demands of the emergency service. Cognitive Radio enhanced ad-hoc architecture has been put forward as a candidate to enable such networks. Spectrum sensing, the cornerstone of the Cognitive Radio paradigm, has been the focus of intensive research, from which the main conclusion was that its performance can be greatly enhanced through the use of cooperative sensing schemes. To apply the Cognitive Radio paradigm to Ad-hoc disaster relief networks, the design of effective cooperative spectrum sensing schemes is essential. In this paper we propose a cluster based orchestration cooperative sensing scheme, which adapts to the cluster nodes surrounding radio environment state as well as to the degree of correlation observed between those nodes. The proposed scheme is given both in a centralized as well as in a decentralized approach. In the centralized approach, the cluster head controls and adapts the distribution of the cluster sensing nodes according to the monitored spectrum state. While in the decentralized approach, each of the cluster nodes decides which spectrum it should monitor, according to the past local sensing decisions of the cluster nodes. The centralized and decentralized schemes can be combined to achieve a more robust cooperative spectrum sensing scheme. The proposed scheme performance is evaluated through a framework, which allows measuring the accuracy of the spectrum sensing cooperative scheme by measuring the error in the estimation of the monitored spectrum state. Through this evaluation it is shown that the proposed scheme outperforms the case where the choice of which spectrum to sense is done without using the knowledge obtained in previous sensing iterations, i.e. a implementation of a blind Round Robin scheme.
The minimal polynomial of sequence obtained from componentwise linear transformation of linear recurring sequence
Venue: arXiv cs.IT 2009
Year: 2009

Let $S=(s_1,s_2,...,s_m,...)$ be a linear recurring sequence with terms in $GF(q^n)$ and $T$ be a linear transformation of $GF(q^n)$ over $GF(q)$. Denote $T(S)=(T(s_1),T(s_2),...,T(s_m),...)$. In this paper, we first present counter examples to show the main result in [A.M. Youssef and G. Gong, On linear complexity of sequences over $GF(2^n)$, Theoretical Computer Science, 352(2006), 288-292] is not correct in general since Lemma 3 in that paper is incorrect. Then, we determine the minimal polynomial of $T(S)$ if the canonical factorization of the minimal polynomial of $S$ without multiple roots is known and thus present the solution to the problem which was mainly considered in the above paper but incorrectly solved. Additionally, as a special case, we determine the minimal polynomial of $T(S)$ if the minimal polynomial of $S$ is primitive. Finally, we give an upper bound on the linear complexity of $T(S)$ when $T$ exhausts all possible linear transformations of $GF(q^n)$ over $GF(q)$. This bound is tight in some cases.
On the issues of building Information Warehouses
Venue: arXiv cs.HC 2009
Year: 2009

While performing knowledge-intensive tasks of professional nature, the knowledge workers need to access and process large volume of information. Apart from the quantity, they also require that the information received is of high quality in terms of authenticity and details. This, in turn, requires that the information delivered should also include argumentative support, exhibiting the reasoning process behind their development and provenance to indicate their lineage. In conventional document-centric practices for information management, such details are difficult to capture, represent/archive and retrieve/deliver. To achieve such capability we need to re-think some core issues of information management from the above requirements perspective. In this paper we develop a framework for comprehensive representation of information in archive, capturing informational contents along with their context. We shall call it the "Information Warehouse (IW)" framework of information archival. The IW is a significant yet technologically realizable conceptual advancement which can support efficiently some interesting classes of applications which can be very useful to the knowledge workers.
A non-interleaving process calculus for multi-party synchronisation
Venue: arXiv cs.LO 2009
Year: 2009

We introduce the wire calculus. Its dynamic features are inspired by Milner's CCS: a unary prefix operation, binary choice and a standard recursion construct. Instead of an interleaving parallel composition operator there are operators for synchronisation along a common boundary and non-communicating parallel composition. The (operational) semantics is a labelled transition system obtained with SOS rules. Bisimilarity is a congruence with respect to the operators of the language. Quotienting terms by bisimilarity results in a compact closed category.
A New Scheduling Algorithms For Real Time Tasks
Venue: arXiv cs.OS 2009
Year: 2009

The main objective of this paper is to develop the two different ways in which round robin architecture is modified and made suitable to be implemented in real time and embedded systems. The scheduling algorithm plays a significant role in the design of real time embedded systems. Simple round robin architecture is not efficient to be implemented in embedded systems because of higher context switch rate, larger waiting time and larger response time. Missing of deadlines will degrade the system performance in soft real time systems. The main objective of this paper is to develop the scheduling algorithm which removes the drawbacks in simple round robin architecture. A comparison with round robin architecture to the proposed architectures has been made. It is observed that the proposed architectures solves the problems encountered in round robin architecture in soft real time by decreasing the number of context switches waiting time and response time thereby increasing the system throughput.
Behavior and performance of the deep belief networks on image classification
Venue: arXiv cs.NE 2009
Year: 2009

We apply deep belief networks of restricted Boltzmann machines to bags of words of sift features obtained from databases of 13 Scenes, 15 Scenes and Caltech 256 and study experimentally their behavior and performance. We find that the final performance in the supervised phase is reached much faster if the system is pre-trained. Pre-training the system on a larger dataset keeping the supervised dataset fixed improves the performance (for the 13 Scenes case). After the unsupervised pre-training, neurons arise that form approximate explicit representations for several categories (meaning they are mostly active for this category). The last three facts suggest that unsupervised training really discovers structure in these data. Pre-training can be done on a completely different dataset (we use Corel dataset) and we find that the supervised phase performs just as good (on the 15 Scenes dataset). This leads us to conjecture that one can pre-train the system once (e.g. in a factory) and subsequently apply it to many supervised problems which then learn much faster. The best performance is obtained with single hidden layer system suggesting that the histogram of sift features doesn't have much high level structure. The overall performance is almost equal, but slightly worse then that of the support vector machine and the spatial pyramidal matching.
On the Energy Efficiency of LT Codes in Proactive Wireless Sensor Networks
Venue: arXiv cs.IT 2009
Year: 2009

This paper presents the first in-depth analysis on the energy efficiency of LT codes with Non Coherent M-ary Frequency Shift Keying (NC-MFSK), known as green modulation [1], in a proactive Wireless Sensor Network (WSN) over Rayleigh flat-fading channels with path-loss. We describe the proactive system model according to a pre-determined time-based process utilized in practical sensor nodes. The present analysis is based on realistic parameters including the effect of channel bandwidth used in the IEEE 802.15.4 standard, and the active mode duration. A comprehensive analysis, supported by some simulation studies on the probability mass function of the LT code rate and coding gain, shows that among uncoded NC-MFSK and various classical channel coding schemes, the optimized LT coded NC-MFSK is the most energy-efficient scheme for distance $d$ greater than the pre-determined threshold level $d_T$, where the optimization is performed over coding and modulation parameters. In addition, although uncoded NC-MFSK outperforms coded schemes for $d < d_T$, the energy gap between LT coded and uncoded NC-MFSK is negligible for $d < d_T$ compared to the other coded schemes. These results come from the flexibility of the LT code to adjust its rate to suit instantaneous channel conditions, and suggest that LT codes are beneficial in practical low-power WSNs with dynamic position sensor nodes.
Practical Algorithmic Techniques for Several String Processing Problems
Venue: arXiv cs.DS 2009
Year: 2009

The domains of data mining and knowledge discovery make use of large amounts of textual data, which need to be handled efficiently. Specific problems, like finding the maximum weight ordered common subset of a set of ordered sets or searching for specific patterns within texts, occur frequently in this context. In this paper we present several novel and practical algorithmic techniques for processing textual data (strings) in order to efficiently solve multiple problems. Our techniques make use of efficient string algorithms and data structures, like KMP, suffix arrays, tries and deterministic finite automata.
Measures of lexical distance between languages
Venue: arXiv cs.CL 2009
Year: 2009

The idea of measuring distance between languages seems to have its roots in the work of the French explorer Dumont D'Urville \cite{Urv}. He collected comparative words lists of various languages during his voyages aboard the Astrolabe from 1826 to 1829 and, in his work about the geographical division of the Pacific, he proposed a method to measure the degree of relation among languages. The method used by modern glottochronology, developed by Morris Swadesh in the 1950s, measures distances from the percentage of shared cognates, which are words with a common historical origin. Recently, we proposed a new automated method which uses normalized Levenshtein distance among words with the same meaning and averages on the words contained in a list. Recently another group of scholars \cite{Bak, Hol} proposed a refined of our definition including a second normalization. In this paper we compare the information content of our definition with the refined version in order to decide which of the two can be applied with greater success to resolve relationships among languages.
Performance Analysis on Molecular Dynamics Simulation of Protein Using GROMACS
Venue: arXiv cs.CE 2009
Year: 2009

Development of computer technology in chemistry, bring many application of chemistry. Not only the application to visualize the structure of molecule but also to molecular dynamics simulation. One of them is Gromacs. Gromacs is an example of molecular dynamics application developed by Groningen University. This application is a non-commercial and able to work in the operating system Linux. The main ability of Gromacs is to perform molecular dynamics simulation and minimization energy. In this paper, the author discusses about how to work Gromacs in molecular dynamics simulation of some protein. In the molecular dynamics simulation, Gromacs does not work alone. Gromacs interact with pymol and Grace. Pymol is an application to visualize molecule structure and Grace is an application in Linux to display graphs. Both applications will support analysis of molecular dynamics simulation.
Gouverner la standardisation par les changements d'arene. Le cas du XML
Venue: arXiv cs.CY 2009
Year: 2009

In this paper, we discuss the available approches of the new governance structures of standardization, in order to propose new hypothesis on the way computer sciences languages are dealt with. We consider the example of the XML language and its applications in order to propose a dynamic analysis of this governance, focusing on the coordination that is done by companies, and the strategic usage they have of these arenas to further their goals. We advocate the development of more of such empirical analysis in order to cover all the perspectives of possible international policies in this area.
Comparative Study of Different Guard Time Intervals to Improve the BER Performance of Wimax Systems to Minimize the Effects of ISI and ICI under Adaptive Modulation Techniques over SUI1 and AWGN Communication Channels
Venue: arXiv cs.NI 2009
Year: 2009

The WIMAX technology based on air interface standard 802.16 wireless MAN is configured in the same way as a traditional cellular network with base stations using point to multipoint architecture to drive a service over a radius up to several kilometers. The range and the Non Line of Sight (NLOS) ability of WIMAX make the system very attractive for users, but there will be slightly higher BER at low SNR. The aim of this paper is the comparative study of different guard time intervals effect for improving BER at different SNR under digital modulation (QPSK, 16QAM and 64QAM) techniques and different communication channels AWGN and fading channels Stanford University Interim (SUI 1) of an WIMAX system. The comparison between these effects with Reed-Solomon (RS) encoder with Convolutional encoder (half) rated codes in FEC channel coding will be investigated. The simulation results of estimated Bit Error Rate (BER) displays that the implementation of interleaved RS code (255,239,8) with (half) rated Convolutional code of 0.25 guard time intervals under QPSK modulation technique over AWGN channel is highly effective to combat in the Wimax communication system. To complete this performance analysis in Wimax based systems, a segment of audio signal is used for analysis. The transmitted audio message is found to have retrieved effectively under noisy situation.
Fingerprint Verification based on Gabor Filter Enhancement
Venue: arXiv cs.CR 2009
Year: 2009

Human fingerprints are reliable characteristics for personnel identification as it is unique and persistence. A fingerprint pattern consists of ridges, valleys and minutiae. In this paper we propose Fingerprint Verification based on Gabor Filter Enhancement (FVGFE) algorithm for minutiae feature extraction and post processing based on 9 pixel neighborhood. A global feature extraction and fingerprints enhancement are based on Hong enhancement method which is simultaneously able to extract local ridge orientation and ridge frequency. It is observed that the Sensitivity and Specificity values are better compared to the existing algorithms.
Ethics Understanding of Software Professional In Risk Reducing Reusability Coding Using Inclusion Set Theory
Venue: arXiv cs.SE 2009
Year: 2009

The technical skill or ability of an individual is different to person in software developments of projects. So, it is necessary to identify the talent and attitude of an individual contribution can be uniformly distributed to the different phases of software development cycle. The line of code analysis metrics to understanding the various skills of the programmers in code development. By using the inclusion set theory of n (AUB) refer to strength and risk free code developed from union of software professionals and system must comprise of achievement of the system goal, effective memory utilization and intime delivery of the product.
Fish recognition based on the combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree
Venue: arXiv cs.CV 2009
Year: 2009

We presents in this paper a novel fish classification methodology based on a combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree. Unlike existing works for fish classification, which propose descriptors and do not analyze their individual impacts in the whole classification task and do not make the combination between the feature selection, image segmentation and geometrical parameter, we propose a general set of features extraction using robust feature selection, image segmentation and geometrical parameter and their correspondent weights that should be used as a priori information by the classifier. In this sense, instead of studying techniques for improving the classifiers structure itself, we consider it as a black box and focus our research in the determination of which input information must bring a robust fish discrimination.The main contribution of this paper is enhancement recognize and classify fishes based on digital image and To develop and implement a novel fish recognition prototype using global feature extraction, image segmentation and geometrical parameters, it have the ability to Categorize the given fish into its cluster and Categorize the clustered fish into poison or non-poison fish, and categorizes the non-poison fish into its family .
A Reliable Replication Strategy for VoD System using Markov Chain
Venue: arXiv cs.MM 2009
Year: 2009

In this paper we have investigated on the reliability of streams for a VoD system. The objective of the paper is to maximize the availability of streams for the peers in the VoD system. We have achieved this by using data replication technique in the peers. Hence, we proposed a new data replication technique to optimally store the videos in the peers. The new data replication technique generates more number of replicas than the existing techniques such as random, minimum request and maximize hit. We have also investigated by applying the CTMC model for the reliability of replications during the peer failures. Our result shows that the mean lifetime of replicas are more under various circumstances. We have addressed the practical issues of efficient utilization of overall bandwidth and buffer in the VoD system. We achieved greater success playback probability of videos than the existing techniques.
An ensemble approach for feature selection of Cyber Attack Dataset
Venue: arXiv cs.CR 2009
Year: 2009

Feature selection is an indispensable preprocessing step when mining huge datasets that can significantly improve the overall system performance. Therefore in this paper we focus on a hybrid approach of feature selection. This method falls into two phases. The filter phase select the features with highest information gain and guides the initialization of search process for wrapper phase whose output the final feature subset. The final feature subsets are passed through the Knearest neighbor classifier for classification of attacks. The effectiveness of this algorithm is demonstrated on DARPA KDDCUP99 cyber attack dataset.